Data Prep
Okay, lets check out the National Student Clearinghouse enrollment data. I will first import the entire dataset, clean it up, and then merge it with the master / NE graduate dataset.
| 10025234 |
20120103 |
20120126 |
00235400 |
H |
002354 |
| 11364693 |
20130122 |
20130517 |
00238881 |
F |
002388 |
| 3090532 |
20090824 |
20091218 |
00238300 |
F |
002383 |
| 11127041 |
20200601 |
20200819 |
00238200 |
L |
002382 |
| 4465956 |
20100823 |
20101217 |
00299700 |
F |
002997 |
| 9191392 |
20110110 |
20110513 |
00553400 |
L |
005534 |
| PersonID |
| EnrollmentBeginTimeID |
| EnrollmentEndTimeID |
| OPEID |
| EnrollmentStatus |
| OPEID.6 |
The dataset has 3,010,265 rows and 6 columns. Here are the columns and their definitions.
- EnrollmentBeginTimeID: Begin date for the student’s period of attendance.
- EnrollmentEndTimeID: End date for the student’s period of attendance.
- OPEID: Office of Postsecondary Education (OPE)/FICE code of the college that the student attended (Foreign Key to IPEDSCharacteristics).
- InstitutionName: Name of institution
- EnrollmentStatus: The last enrollment status reported for the student. This field will have ‘N/A’ or “NULL” if the reporting college has not defined the student’s enrollment status as directory information. Here are the code definitions;
- F: Full-time
- Q: Three-quarter time
- H: Half-time
- L: Less than half-time
- A: Leave of absence
- W: Withdrawn
- D: Deceased
Essentially, this dataset provides semester-based information - each observation is a semester/single period with the institutional information and beginning and end time of that single period (usually a semester). For example, if an individual attended South Dakota State University, attended in a “typical” fashion (fall and spring semesters), and graduated in 4 years, there would be 8 observations for that PersonID - one observation per semester.
There are a few pieces of information that I want to consolidate from this dataset;
- Did the PersonID in the master dataset attend a college - yes or no?
- Did the PersonID attend college during high school (PSEO), immediately after graduation or wait?
- Did the PersonID attend multiple colleges - yes or no?
- What type of college did the PersonID attend first?
- Did the PersonID attend a public, private not-for-profit, or private or, for-profit college(s)?
- At any point during their college career, did the PersonID attend a college(s) inside or outside of the Southwest Region or outside the EDR of their high school?
I was going to include the length of time that a PersonID attended college, but due to differences in how institutions report enrollment periods, it didn’t seem like it would be a great indicator.
The pieces of information missing in the original dataset is location and type of institution attended.To do this we will need to join the original dataset with an IPEDS dataset using opeid.
The IPEDS data and the NSC data will only match using the first 6 digits of the IPEDS OPEID values. So I will change that in the NSC data. Then I can join.
| 10025234 |
20120103 |
20120126 |
00235400 |
H |
002354 |
173665 |
Saint Paul |
MN |
27 |
4 |
27123 |
2 |
Hamline University |
| 11364693 |
20130122 |
20130517 |
00238881 |
F |
002388 |
174233 |
Duluth |
MN |
27 |
4 |
27137 |
1 |
University of Minnesota-Duluth |
| 3090532 |
20090824 |
20091218 |
00238300 |
F |
002383 |
174862 |
Saint Bonifacius |
MN |
27 |
4 |
27019 |
2 |
Crown College |
| 11127041 |
20200601 |
20200819 |
00238200 |
L |
002382 |
174844 |
Northfield |
MN |
27 |
4 |
27131 |
2 |
St Olaf College |
| 4465956 |
20100823 |
20101217 |
00299700 |
F |
002997 |
200332 |
Fargo |
ND |
38 |
4 |
38017 |
1 |
North Dakota State University-Main Campus |
| 9191392 |
20110110 |
20110513 |
00553400 |
L |
005534 |
174756 |
Saint Cloud |
MN |
27 |
4 |
27145 |
4 |
St Cloud Technical and Community College |
| PersonID |
| EnrollmentBeginTimeID |
| EnrollmentEndTimeID |
| OPEID |
| EnrollmentStatus |
| OPEID.6 |
| Unitid |
| City |
| State |
| FIPS |
| GeographicRegion |
| CountyCode |
| InstitutionSector |
| InstitutionName |
After joining these we now have 3,009,843 rows and 14 columns. This is a few less than the original enrollment document due to a few OPEIDs not aligning. But overall, the data now has the institution sector that the individual attended as well as location.
First, we will create a dataset with a column confirming their post-secondary attendance for each unique PersonID in the nsc.enrollment.ipeds.
| 10025234 |
Yes |
| 11364693 |
Yes |
| 3090532 |
Yes |
| 11127041 |
Yes |
| 4465956 |
Yes |
| 9191392 |
Yes |
There are 397,372 rows and 2 columns in the dataset. The columns provide the unique PersonID along with a newly created column confirming that they attended post-secondary education.
Next we will extract the first month and year each PersonID attended a post-secondary institution which will help us figure out how long after high school graduation did they wait until they attended a post-secondary institution.
| 3090532 |
20090824 |
| 6790768 |
20080825 |
| 9172836 |
20060906 |
| 1597967 |
20060905 |
| 5583982 |
20100823 |
| 5456903 |
20230109 |
This dataset has 397,372 rows and 2 columns. Essentially, this dataset provides each unique PersonID with the earliest begin time for post-secondary education.
Next we will determine how many different institiutions the PersonID attended during their post-secondary career.
| 14 |
1 |
| 98 |
1 |
| 141 |
1 |
| 198 |
2 |
| 208 |
1 |
| 227 |
2 |
As expected, we have 397,372 rows and 2 columns. Each PersonID in the dataset has the number of unique post-secondary institutions they attended.
Next, we will create a dataset indicating the type of college the PersonID attended first. I will use the IPEDS sector data. Here are the definitions of the institution sector;
- 0 - Administrative Unit
- 1 - Public, 4-year or above
- 2 - Private not-for-profit, 4-year or above
- 3 - Private for-profit, 4-year or above
- 4 - Public, 2-year
- 5 - Private not-for-profit, 2-year
- 6 - Private for-profit, 2-year
- 7 - Public, less-than 2-year
- 8 - Private not-for-profit, less-than 2-year
- 9 - Private for-profit, less-than 2-year
- 99 - Sector unknown (not active)
| 3090532 |
2 |
| 6790768 |
4 |
| 9172836 |
2 |
| 1597967 |
1 |
| 5583982 |
4 |
| 5456903 |
4 |
| PersonID |
| InstitutionSector |
As expected, we have 397,372 rows and 2 columns. This dataset now provides the sector of the first post-secondary institution they attended immediately after college.
Next we will determine thy type(s) of college(s) they attended during their post-secondary career. To do this we will first create a dataset with columns for each institution sector and a confirmation indicator on whether the PersonID attended that particular sector at some point in their career. I will then create a new category indicating that attended more than one type of institution sector. Once completed, I will be left with a dataset that has a column for each unique PersonID and what sector they attended, as well as a newly created code for “attended more than 1 type of sector”.
| 227 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 768 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
| 1276 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 2432 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 2688 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
| 2872 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| PersonID |
| ps.sector.1 |
| ps.sector.2 |
| ps.sector.3 |
| ps.sector.4 |
| ps.sector.5 |
| ps.sector.6 |
| ps.sector.7 |
| ps.sector.8 |
| ps.sector.9 |
As expected, we have 28,788 rows and 10 columns. This dataset provides each unique PersonID with the institution sector they attended using the codes listed above. For PersonID’s that attended multiple sectors, they were coded as “10”.
Next we will determine whether they attended a post-secondary institution inside or outside of the planning region, outside their EDR, or outside of Minnesota. Since many of the PersonID in the dataset have attended multiple institutions, we will categorize it in the following way in order to capture the combinations of attendance;
- Attended inside region only (planning region, EDR, RUCA, State)
- Attended inside and outside region (planning region, EDR, RUCA, state)
- Attended outside region only (planning region, EDR, RUCA, state)
In order to do this we will need to combine our planning region and EDR joining documents with the nsc.enrollment.ipeds dataset. We will also need to join the master dataset with it to determine the location of the PersonID’s high school graduation location. Lastly, we will need to join up the RUCA categories for counties outside of Minnesota. Then we can start the categorization process.
| 11127041 |
St Olaf College |
27131 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
Urban/town/rural mix |
Northeast |
131 |
27 |
EDR 10 - Southeast |
Southeast |
| 10554185 |
Quinsigamond Community College |
25027 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
Urban/town/rural mix |
Northeast |
027 |
25 |
NA |
NA |
| 4577203 |
University of St Thomas |
27123 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
Urban/town/rural mix |
Northeast |
123 |
27 |
EDR 11 - 7 County Twin Cities |
Seven County Mpls-St Paul |
| 1607818 |
Central Lakes College-Brainerd |
27035 |
Town/rural mix |
EDR 3 - Arrowhead |
Town/rural mix |
Northeast |
035 |
27 |
EDR 5 - North Central |
Northwest |
| 11986653 |
University of St Thomas |
27123 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
Urban/town/rural mix |
Northeast |
123 |
27 |
EDR 11 - 7 County Twin Cities |
Seven County Mpls-St Paul |
| 7503238 |
University of Minnesota-Twin Cities |
27053 |
Entirely urban |
EDR 3 - Arrowhead |
Urban/town/rural mix |
Northeast |
053 |
27 |
EDR 11 - 7 County Twin Cities |
Seven County Mpls-St Paul |
| PersonID |
| InstitutionName |
| CountyCode |
| ps.Dem_Desc |
| grad.edr |
| grad.ruca |
| grad.pr |
| ps.countyfp |
| ps.statefp |
| ps.edr |
| ps.pr |
This joined dataset gives us 143,668 rows and 11 columns. The columns beginning with “ps” are the ruca category and regions of the post-secondary institution attended. The columns beginning with “grad” are the ruca category and regions of the highschool from which the PersonID graduated.
One thing that’s important is to realize that joining the nsc.enrollment dataset with the SW graduates dataset means we will have some NAs. Some of the students listed in the nsc.enrollment dataset aren’t in the SW graduates dataset since they may have graduated before 2008 or didn’t actually meet the criteria in the SW graduates dataset. Therefore, as we move forward, I will need to make sure to remove those NAs.
From here we can start creating new columns beginning with the RUCA category. to create this column we will gather each PersonID to examine whether or they attended a post-secondary institution in the same RUCA category, or if they attended multiple post-secondary insitutions with one institution in the same category and another not the same.
| 227 |
In same RUCA |
| 768 |
Outside RUCA |
| 1276 |
Inside and outside same RUCA |
| 2432 |
In same RUCA |
| 2688 |
Outside RUCA |
| 3601 |
Outside RUCA |
So there are fewer observations in this dataset than previous subsets. Why? This is due to dropping and PersonID that wasn’t in the Southwest graduate dataset. I did not do that for the previous subsets. However, those previous subsets will be filtered down once we join it with the master dataset.
This dataset provides the PersonID and whether they attended a post secondary institution in a location with the same, outside, or both outside and inside (if attended multiple post-secondary institutions) RUCA categories of their high school from which they graduated. There are 19,931 rows and 2 columns.
Up next we will create a dataset indicating whether a PersonID that graduated from a Southwest MN high school attended a post-secondary insitution in their high school’s EDR.
| 227 |
Outside EDR |
| 768 |
In same EDR |
| 1276 |
Inside and outside same EDR |
| 2432 |
Outside EDR |
| 2688 |
Outside EDR |
| 3601 |
Outside EDR |
As expected there are 19,931 rows and 2 columns.
Next we will determine which Southwest MN graduates attended a post-secondary institution in the same planning region (Southwest planning region).
| 227 |
Outside PR |
| 768 |
In same PR |
| 1276 |
Inside and outside same PR |
| 2432 |
Outside PR |
| 2688 |
Outside PR |
| 3601 |
Outside PR |
As expected, there are 19,931 rows and 2 columns. This dataset provides whether the post-secondary institution(s) attended were in the same planning region as the high school from whichh they graduates, outside of the planning region, or both (attended multiple institutions).
Next we want to see how many of the students leave the state to attend post-secondary education.
| 227 |
Inside and outside MN |
| 768 |
In MN |
| 1276 |
Inside and outside MN |
| 2432 |
Outside MN |
| 2688 |
In MN |
| 3601 |
In MN |
As expected we have 19,931 rows and 2 columns. This dataset provides each distinct PersonID with whether they attended post secondary institutions inside MN, outside MN, or both.
Okay, now it’s time to join all of these with the master dataset. I will also create a new column that confirms whether they attended a college immediately (within the first year) after graduating high school.
| 6140596 |
15068 |
CHERRY SECONDARY |
St. Louis |
137 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
2017 |
Pre-covid grad |
F |
N |
Y |
1 |
1 |
0 |
0 |
White |
4 |
15 |
0 |
6 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
0 |
0 |
CTE Concentrator or Completor |
0 |
2 |
3 |
2 |
0 |
Yes |
20170821 |
2017 |
0 |
Yes |
1 |
4 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
In same RUCA |
In same EDR |
In same PR |
In MN |
| 1986818 |
207340 |
ALC Independent Study |
St. Louis |
137 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
2022 |
Post-covid grad |
M |
N |
Y |
0 |
0 |
0 |
0 |
Unknown |
2 |
NA |
0 |
7 |
0 |
0 |
1 |
0 |
2 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
CTE Concentrator or Completor |
0 |
3 |
3 |
2 |
0 |
No |
NA |
NA |
NA |
NA |
0 |
NA |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NA |
NA |
NA |
NA |
| 8332752 |
67509 |
EAST HIGH SCHOOL |
St. Louis |
137 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
2016 |
Pre-covid grad |
M |
N |
N |
0 |
1 |
0 |
0 |
White |
3 |
26 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
CTE Participant |
0 |
4 |
4 |
4 |
0 |
Yes |
20160906 |
2016 |
0 |
Yes |
2 |
1 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
Inside and outside same RUCA |
Inside and outside same EDR |
Inside and outside same PR |
In MN |
| 6569729 |
49396 |
AITKIN SECONDARY SCHOOL |
Aitkin |
001 |
Entirely rural |
EDR 3 - Arrowhead |
2012 |
Pre-covid grad |
F |
N |
N |
0 |
1 |
0 |
0 |
White |
3 |
24 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
No CTE |
0 |
4 |
4 |
3 |
0 |
Yes |
20120829 |
2012 |
0 |
Yes |
2 |
2 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Outside RUCA |
Outside EDR |
Outside PR |
In MN |
| 322283 |
43867 |
MESABI EAST SECONDARY |
St. Louis |
137 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
2015 |
Pre-covid grad |
M |
N |
N |
0 |
1 |
0 |
0 |
White |
3 |
22 |
1 |
3 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
CTE Participant |
0 |
4 |
4 |
4 |
0 |
Yes |
20150826 |
2015 |
0 |
Yes |
4 |
1 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
Inside and outside same RUCA |
Inside and outside same EDR |
Inside and outside same PR |
In MN |
| 2657233 |
211618 |
HERMANTOWN SENIOR HIGH |
St. Louis |
137 |
Urban/town/rural mix |
EDR 3 - Arrowhead |
2023 |
Post-covid grad |
F |
N |
N |
0 |
1 |
0 |
0 |
Unknown |
3 |
27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
No CTE |
0 |
4 |
4 |
3 |
0 |
Yes |
20230830 |
2023 |
0 |
Yes |
1 |
2 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Outside RUCA |
Outside EDR |
Outside PR |
Outside MN |
| PersonID |
| K12OrganizationID |
| OrganizationName |
| county.name |
| countyfp |
| Dem_Desc |
| edr |
| grad.year |
| grad.year.covid |
| Gender |
| LimitedEnglishProficiencyIndicator |
| HomelessIndicator |
| economic.status |
| pseo.participant |
| SpecialEdStatus |
| non.english.home |
| RaceEthnicity |
| n.years.attended |
| ACTCompositeScore |
| ap.exam |
| total.cte.courses.taken |
| ACTE-SPED |
| Agriculture, Food, and Natural Resources |
| Architecture & Construction |
| Arts, A/V Technology & Communication |
| Business, Management & Administrative |
| Diversified |
| Education & Training |
| Finance |
| Government & Public Administration |
| Health Science |
| Hospitality & Tourism |
| Human Services |
| Information Technology |
| Law, Public Safety & Security |
| Manufacturing |
| Marketing |
| Program Area (Diversified) |
| Science, Technology, Engineering, & Mathematics |
| STEM |
| Transportation, Distribution & Logistics |
| Work Experience-Handicapped |
| Work Experience-Handicapped (16-20+ on IEP) |
| Work Experience/Career Exploration (age 14-15 on I |
| Work-Experience-Disadvantaged |
| Youth Apprenticeship |
| cte.achievement |
| english.learner |
| MCA.M |
| MCA.R |
| MCA.S |
| sat.taken |
| attended.ps |
| first.attend.ps |
| attended.ps.first.year |
| attended.ps.years.hsgrad |
| attended.ps.within.first.year.hsgrad |
| n.institutions |
| first.InstitutionSector |
| ps.sector.1 |
| ps.sector.2 |
| ps.sector.3 |
| ps.sector.4 |
| ps.sector.5 |
| ps.sector.6 |
| ps.sector.7 |
| ps.sector.8 |
| ps.sector.9 |
| ps.in.same.ruca |
| ps.in.same.edr |
| ps.in.same.pr |
| ps.in.MN |
As expected, we have the same number of rows as the original master dataset - 28,788 rows. Here are the explanations of all the new columns added to the master dataset.
- attended.ps - this is a confirmation that the PersonID attended a post-secondary institution at some point after graduating high school.
- attended.ps.years.hsgrad - the number of years after graduation that a PersonID attended a post-secondary education. In some cases, the value is negative indicating that the PersonID attended a post-secondary institution before graduating high school.
- attended.ps.within.first.year.hsgrad - did the PersonID attend a post-secondary education institution within 1 year or less from graduating high school.
- n.institutions - how many post-secondary institutions the PersonID attended
- first.InstitutionSector - the sector of the first post-secondary institution attended.
- InstitutionSector - The sector of the post-secondary institution attended (could be multiple)
- ps.in.same.ruca - did the PersonID attend a post-secondary institution in the same RUCA category as their high school
- ps.in.same.edr - did the PersonID attend a post-secondary institution in the same economic development region as high school
- ps.in.same.pr - did the PersonID attend a post-secondary institution in the same planning region as high school
- ps.in.MN = did the PersonID attend a post-secondary institution in Minnesota.
Summary of students attending post-secondary
Lets summarize the percentage of students that attended a post-secondary institution and compare across RUCA categories.
Below is the percentage of students that attended a post-secondary institution from the entire dataset. 69% of the PersonIDs in the dataset attended a post-secondary institution at some point between 2007 and 2024.
Now lets check to see if this percentage is statistically significantly different by RUCA group.
The crosstabs do inidcate that there is a statistically significant difference in the percentage of graduates that attend a post-secondary institution by high school RUCA category. However, the percentages aren’t that different. The biggest difference is that graduates from a town/rural mix district attend at a lower rate - 67.1% compared to 69.5% and 69.8%.
Cell Contents
|-------------------------|
| N |
| Expected N |
| N / Row Total |
|-------------------------|
Total Observations in Table: 28788
| master.9$attended.ps
master.9$Dem_Desc | No | Yes | Row Total |
---------------------|-----------|-----------|-----------|
Entirely rural | 523 | 1192 | 1715 |
| 527.642 | 1187.358 | |
| 0.305 | 0.695 | 0.060 |
---------------------|-----------|-----------|-----------|
Town/rural mix | 1923 | 3916 | 5839 |
| 1796.444 | 4042.556 | |
| 0.329 | 0.671 | 0.203 |
---------------------|-----------|-----------|-----------|
Urban/town/rural mix | 6411 | 14823 | 21234 |
| 6532.914 | 14701.086 | |
| 0.302 | 0.698 | 0.738 |
---------------------|-----------|-----------|-----------|
Column Total | 8857 | 19931 | 28788 |
---------------------|-----------|-----------|-----------|
Statistics for All Table Factors
Pearson's Chi-squared test
------------------------------------------------------------
Chi^2 = 16.22274 d.f. = 2 p = 0.0003001067
Summary of years between graduation and attending post-secondary
Now lets take a look at the number of years between high school graduation and attending post-secondary. We will begin with the total number of students before diving into differences across RUCA categories and regions.
A huge majority of students that attended a post secondary institution waited less than 1 year after graduating high school (83%)
Next lets check to see if there are any differences in the numbers of years between high school and attending post secondary by RUCA category.
The ANOVA table indicates there are significant differences between the means across RUCA categories with a pvalue = .0164. However, in the TukeyHSD test, it only shows that the statistically significant difference exists between urban/town/rural mix and town/rural mix districts.
It says that graduates from urban/town/rural districts wait to attent post-secondary institution .05 years longer than town/rural mix districts.
Df Sum Sq Mean Sq F value Pr(>F)
Dem_Desc 2 9 4.577 4.114 0.0164 *
Residuals 19928 22171 1.113
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = attended.ps.years.hsgrad ~ Dem_Desc, data = years.between.grad.ps.anova.ruca)
$Dem_Desc
diff lwr upr
Town/rural mix-Entirely rural -0.07049636 -0.152278281 0.01128555
Urban/town/rural mix-Entirely rural -0.01919931 -0.093629415 0.05523079
Urban/town/rural mix-Town/rural mix 0.05129705 0.006877399 0.09571670
p adj
Town/rural mix-Entirely rural 0.1073191
Urban/town/rural mix-Entirely rural 0.8176019
Urban/town/rural mix-Town/rural mix 0.0186491
Summary of number of colleges attended
Next we will summarize the number of colleges attended by producing the summary statistics and distribution.
The table and distribution chart below show that a large majority (57.1%) of individuals attended only one post secondary institutions followed by 28% attending two.
I’m not sure it’s all that important knowing whether there are differences in the percentage of students and the number of institutions they’ve attended across RUCA categories or regions. So we will skip that analysis for now.
Summary of types of first college attended
I think this will be very interesting - we want to see what they breakdown is of students attending different types of colleges. We will start by summarizing the total dataset before looking at differences across RUCA categories and regions.
The chart below provides the percentage of students in the entire dataset that attended each institution sector. By far, a huge majority attend either a public 4-year (31%) or public 2-year (53%).
Next we will check to see if those percentages are significantly different by RUCA category of the graduates high school.
The crosstabs indicate that there is a significant difference in the percentage of students attending different institution sectors depending on the RUCA category of their high school. The p-value was 2.573119e-22.
The primary differences are;
- Town/rural mix high school graduates attended public, 2-year institutions at a significantly higher rate than entirely rural and urban/town/rural mix students.
- Entirely rural and urban/town/rural mix graduates attend private, not-for-profit 4-year institutions at a higher rate.
Cell Contents
|-------------------------|
| N |
| Expected N |
| N / Row Total |
|-------------------------|
Total Observations in Table: 19850
| first.college.sector.ct.1.ruca$first.InstitutionSector
first.college.sector.ct.1.ruca$Dem_Desc | 1 | 2 | 3 | 4 | Row Total |
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
Entirely rural | 370 | 183 | 8 | 617 | 1178 |
| 366.337 | 177.086 | 9.080 | 625.497 | |
| 0.314 | 0.155 | 0.007 | 0.524 | 0.059 |
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
Town/rural mix | 1156 | 400 | 39 | 2307 | 3902 |
| 1213.453 | 586.578 | 30.076 | 2071.893 | |
| 0.296 | 0.103 | 0.010 | 0.591 | 0.197 |
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
Urban/town/rural mix | 4647 | 2401 | 106 | 7616 | 14770 |
| 4593.210 | 2220.337 | 113.844 | 7842.610 | |
| 0.315 | 0.163 | 0.007 | 0.516 | 0.744 |
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
Column Total | 6173 | 2984 | 153 | 10540 | 19850 |
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
Statistics for All Table Factors
Pearson's Chi-squared test
------------------------------------------------------------
Chi^2 = 114.2895 d.f. = 6 p = 2.573119e-22
Summary of attending college with same RUCA category
Up next is determining how many students attended a post-secondary institution located in a county with the same RUCA category as their high school. First we will look at the percentages of the total dataset and then we will break it up.
The chart below shows that nearly half (56.3%) of students graduating from a NE MN high school attended a post secondary institution that was located in a county with the same RUCA category as their high school.
Next, lets check to see if the type of RUCA category they graduated from is related to whether the college they attend is in the same or different RUCA category.
The crosstabs indicate that there is a relationship in the location of the individuals high school graduation and the RUCA category of their post-secondary institution(s). The p-valu was 0.
As expected, individuals who graduate from a high school in an entirely rural county category were significantly more likely to attend a post secondary institution that wasn’t entirely rural (100% actually). Individuals that graduated from a school districte located in a Urban/town/rural mix county were significantly more likely to attend a college in the same RUCA category.
Cell Contents
|-------------------------|
| N |
| Expected N |
| N / Row Total |
|-------------------------|
Total Observations in Table: 19930
| ps.same.ruca.ct.ruca$ps.in.same.ruca
ps.same.ruca.ct.ruca$Dem_Desc | In same RUCA | Inside and outside same RUCA | Outside RUCA | Row Total |
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
Entirely rural | 0 | 0 | 1192 | 1192 |
| 670.941 | 219.440 | 301.618 | |
| 0.000 | 0.000 | 1.000 | 0.060 |
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
Town/rural mix | 915 | 1024 | 1977 | 3916 |
| 2204.199 | 720.913 | 990.888 | |
| 0.234 | 0.261 | 0.505 | 0.196 |
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
Urban/town/rural mix | 10303 | 2645 | 1874 | 14822 |
| 8342.860 | 2728.646 | 3750.494 | |
| 0.695 | 0.178 | 0.126 | 0.744 |
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
Column Total | 11218 | 3669 | 5043 | 19930 |
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
Statistics for All Table Factors
Pearson's Chi-squared test
------------------------------------------------------------
Chi^2 = 6783.581 d.f. = 4 p = 0
Summary of attending college in same planning region as high school
Now we want to see how many students attend a college that is located in the same planning region as their high school.
The chart below shows that nearly 44% of students graduating from NE MN High schools leave the region to attend post secondary education.
Summary of attending college in Minnesota
Now we want to see how many students stay or leave Minnesota to attend college.
The chart below shows that 59% of students graduating from SW MN high schools attend a post secondary institution in Minnesota.
Next, lets check to see if the RUCA category of their high school is related to whether they attend a college inside or outside Minnesota.
The crosstabs below indicate that there is a relationship between the RUCA category of a student’s high school and whether they attend a college inside or outside of Minnesota. The p-value was .0002.
The primary difference is that as districts become more urban, the percentage of graduates that attend a college in Minnesota declines - 69% in urban/town/rural mix districts compared to 73% in entirely rural districts.
Cell Contents
|-------------------------|
| N |
| Expected N |
| N / Row Total |
|-------------------------|
Total Observations in Table: 19931
| ps.in.MN.ct.ruca$ps.in.MN
ps.in.MN.ct.ruca$Dem_Desc | In MN | Inside and outside MN | Outside MN | Row Total |
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
Entirely rural | 875 | 177 | 140 | 1192 |
| 831.667 | 203.222 | 157.111 | |
| 0.734 | 0.148 | 0.117 | 0.060 |
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
Town/rural mix | 2812 | 642 | 462 | 3916 |
| 2732.221 | 667.632 | 516.147 | |
| 0.718 | 0.164 | 0.118 | 0.196 |
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
Urban/town/rural mix | 10219 | 2579 | 2025 | 14823 |
| 10342.112 | 2527.146 | 1953.741 | |
| 0.689 | 0.174 | 0.137 | 0.744 |
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
Column Total | 13906 | 3398 | 2627 | 19931 |
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
Statistics for All Table Factors
Pearson's Chi-squared test
------------------------------------------------------------
Chi^2 = 21.62734 d.f. = 4 p = 0.0002377214
What states are students going to college
The last piece here is to provide a map of the United States to see where students are going for college.
Outside of Minnesota, SW MN graduates attend colleges in South Dakota at the highest rate - 18.1%. This is followed by North Dakota at 6.2%.